Name
Title | ||
|---|---|---|
Machine Learning Approach to Tuning Distributed Operating System Load Balancing Algorithms |
Abstract | ||
|---|---|---|
This work concerns the use of machine learning techniques (genetic algorithms) to optimize load balancing policies in the openMosix distributed operating system. Parameters/alternative algorithms in the openMosix kernel were dynamically altered/selected based on the results of a genetic algorithm fitness function. In this fashion optimal parameter settings and algorithms choices were sought for the loading scenarios used as the test cases. |
Year | Venue | Keywords |
|---|---|---|
2006 | ISCA PDCS | machine learning,genetic algorithm,fitness function,load balance |
Field | DocType | Citations |
Kernel (linear algebra),Distributed operating system,Active learning (machine learning),Computer science,Load balancing (computing),Fitness function,Test case,Artificial intelligence,Machine learning,Genetic algorithm,Distributed computing | Conference | 0 |
PageRank | References | Authors |
0.34 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Michael Meehan | 1 | 3 | 1.81 |
| Alan Ritter | 2 | 1312 | 57.28 |